The Key Theorem and the Bounds on the Rate of Uniform Convergence of Statistical Learning Theory on Quasi-Probability Spaces
Ha Ming · Chinese Journal of Computers · 2008
Some properties of quasi-probability are further discussed.The definitions and properties of quasi-random variable and its distribution function,expected value and variance are then presented.Markov inequality,Chebyshev's inequality and the Khinchine's law of large numbers on quasi-probability spaces are also proved.Then the key theorem of learning theory on quasi-probability spaces is proved,and the bounds on the rate of uniform convergence of learning process on quasi-probability spaces are constructed.The investigations will help lay essential theoretical foundations for the systematic and comprehensive development of the quasi-statistical learning theory.